\section{Motivation}
\label{sec:motivation}



\subsection{Analysis of LPL}
\label{subsec:analysisThreshold}
To study the performance of existing LPL, we conduct series experiments in an office environment.
We deployed three TelosB nodes, on channel 22 which overlaps with WiFi channel 11.
One acts as sender and sends out a packet every 10 seconds; one acts as receiver and runs the fixed-threshold method in default BoX-MAC-2; the last one acts also acts as receiver and runs the adaptive-threshold method in ADEP \cite{bib:IPSN13EnergyLPL}.
The sleep interval of both receivers is 512 ms.
We record the number of wakeups and the number of received packets.
Then, we can measure the number of false wakeups as their subtraction.
The information is reported every 5120 ms.

We plot the false wakeup ratios of these two methods in Figure \ref{fig:motivationOfficeThreshold}, together with the threshold adopted in ADEP during the experiment.
To guarantee the network performance is not influenced, ADEP's adaptive algorithm does not always use the optimal threshold which is the minimum RSSI of incoming links.
ADEP also periodically set the threshold to minimum for not ignoring new coming nodes.
Here, we simply let the adaptive-threshold method employ the optimal threshold all the time.
Therefore, the result represents the optimal false wakeup ratio that ADEP can achieve.
When RSSI between nodes is high, e.g., [-55dBm, -40dBm ], the false wakeup ratio of ADEP is low since the threshold can be set higher than the RSSI of interference.
However, when optimal threshold is in [-77dBm, -60dBm], the false wakeup ratio is still very high, even better than the fixed-threshold method.
Despite the adaptive threshold filters out some false wakeups incurred by interference with low RSSI, it loses effectiveness when encountering interference with high RSSI.


\begin{figure*}
\centering
\begin{minipage}[t]{0.245\linewidth}
\centering
\includegraphics[width=1.8in]{figure/MotivationDutyCycleADEPandDefualt.pdf}
\caption{False wakeup ratio of threshold-based LPL in office environment}
\label{fig:motivationOfficeThreshold}
\end{minipage}
\begin{minipage}[t]{0.245\linewidth}
\includegraphics[width=1.8in]{figure/M1DefaultVsADEP.pdf}
\caption{False wakeup ratio of threshold-based LPL in controlled environment}
\label{fig:motivationThreshold}
\end{minipage}
\begin{minipage}[t]{0.245\linewidth}
\centering
\includegraphics[width=1.8in]{figure/CDFofRSSITestBed.pdf}
\caption{CDF of RSSI in various real deployments}
\label{fig:motivationRSSIDistribution}
\end{minipage}
\begin{minipage}[t]{0.245\linewidth}
\centering
\includegraphics[width=1.8in]{figure/dutyCycleAMAC.pdf}
\caption{Duty cycle of A-MAC under various link qualities}
\label{fig:dutyCycleAMAC}
\end{minipage}
\end{figure*}



To further validate our observation about the limitation of adaptive-threshold method,  we repeat above experiment under various controlled environments in an dormitory.
We deploy the nodes on channel 12 at a varying distances from the interference sources.
%One uses a fixed-threshold consistent to the default setting of BoX-MAC, while another one uses an adaptive threshold.
%Since the optimal threshold of adaptive method is the minimum RSSI of the incoming links, receiver measures the RSSI of 10 received packets from a three-meters away sender, just before each run of the tests and uses this optimal threshold, the average RSSI of received packets.
We use a pair of 802.11n devices as interference sources, running on WiFi channel 1, which is overlapped with ZigBee channel 12.
One is a TP-LINK access point and the other is a Lenovo T430s laptop.
%To measure the false wakeup accurately, we intent to keep ZigBee sender silent without packet sending out during an experiment.
%Hence, any wakeup of the receiver is a false wakeup.
%We record the number of false wakeups of the nodes with two settings at the same time.
%We repeat the experiments 20 times on each location, each of which takes 100 seconds.
%
%\begin{figure}[t]
%\centering
%\includegraphics[width=2.7in]{figure/draft-3.pdf}
%\caption{Performance of threshold-based methods}
%\label{fig:motivationThreshold}
%\end{figure}


%Figure \ref{fig:motivationThreshold} (a) plots the receiver's false wakeup rate under the dormitory occupants' normal activities.
Figure \ref{fig:motivationThreshold} depicts the false wakeup rate under the controlled environment where 5 Mbps UDP traffic is generated between the interference devices by LanTraffic V2 \cite{bib:lanTraffic}.
%Figures reveal the limitation of threshold-based methods.
The fixed-threshold methods are prone to the noise since the cross-technique interference easily exceeds the default threshold even the distance is far.
The adaptive-threshold methods have some resistibility to the strong noise, but very limited.
In the figure, there exists a changing point of false wakeup ratio, which is the result of the limited range of thresholds.
When the distance is larger than $D_{t}$, ADEP works well since RSSI of interference is smaller than the minimum effective RSSI.
However, when the distance is smaller than $D_{t}$, it works as badly as the fixed-threshold method since the interference is stronger than ZigBee links.


%From the figures, we can find out there exists a changing point of false wakeup rate.
%Actually, this changing point is the maximum threshold could be adopted in ADEP, which is also the minimum RSSI of the receiver's incoming links.
%If threshold is set larger than this RSSI, transmissions on this link will be ignored.
%Adaptive-threshold method reduces the false wakeup rate when noise is weaker than this optimal threshold.
%However, when noise is stronger than the optimal threshold that could be achieved, adaptive-threshold method just behaves as poor as the fixed-threshold methods.

%We could understand the limitation of threshold-based methods from above experiments.
In a word, the limitation of threshold-based methods is:
the useable threshold is bound in certain scope, while the strength of noise is unlimited.
Hence, when noise is stronger than the threshold, any effort is in ruin and the false wakeup problem is inevitable.
If we could distinguish whether the ongoing transmission is ZigBee or not regardless of the strength of interference, we could wake up node only when potential packets exist, conquering the false wakeup problem thoroughly.


%\subsection{Forecast of adaptive-threshold methods in real systems}
%\label{subsec:forecastThreshold}

%
%\begin{figure}[t]
%\centering
%\includegraphics[width=3in]{figure/CDFofRSSITestBed.pdf}
%\caption{CDF of RSSI in various deployments}
%\label{fig:motivationRSSIDistribution}
%\end{figure}


Actually, the number of false wakeups that adaptive-threshold methods can avoid depends on the scope of adoptable threshold.
Denote the range of adoptable threshold of a node is $[T_{min}, T_{max}]$, where $T_{max}$ is the minimum RSSI of incoming links of this node.
Hence, the improvement of adaptive-threshold methods is at most the false wakeups caused by the interference with RSSI smaller than $T_{max}$.
%Unfortunately, the high threshold is not easy to achieve.
To get a high threshold for good performance of ADEP, a high minimum RSSI of incoming links must be guaranteed, which requires the network to be dense or the transmitting power to be very high.
%While the energy costed by high-power transmissions can be much more than the wasted energy brought by false wakeup problem, especially under high date rate.

%To save the limited energy resource, the high transmitting power is usually not used.
%However, we claim this improvement may be not enough since interference tends to have a stronger power.
%This is because interference technologies usually have a stronger transmitting power and the limited distance between a interference source to a node in a indoor environment usually does not result in that much attenuation to make the strength of interference be weaker than the minimum RSSI of the effective ZigBee links.
%Besides, to save the limited energy resource, the high transmitting power is usually not used.

%Based on the results of the measurement of an indoor system presented in \cite{bib:SING_levis_2006}, if a link is -87dBm or stronger, it is almost but not completely certain to have a $PRR\geq99\%$.
%That means larger RSSI consumes additional energy without much improvement of PRR since a link with RSSI larger than -87dBm is enough to provide a satisfied quality for delivering packets in an indoor system.
%Hence, the minimum RSSI of effective incoming links could not be high enough to provide a good $T_{max}$ to overcome all the false wakeup problem.

%Figure \ref{fig:motivationRSSIDistribution} presents the RSSI of all links in various deployments of Mirage testbed \cite{bib:mirage}, which is a dense indoor WSN.
%The dataset of these deployments come from SING \cite{bib:SING_levis_2006}.
Unfortunately, the high RSSI is not easily obtained in real deployed systems.
Figure \ref{fig:motivationRSSIDistribution} presents the RSSI of links in SING dataset \cite{bib:SING_levis_2006}, collected from various indoor deployments of Mirage testbed \cite{bib:mirage}.
%RSSI distributions of various deployments reveal that the high RSSI is not easy to achieve in real deployed systems even the maximum transmitting power is employed.
Even when the transmitting power is set to the maximum power lever, $power=31$, all links have RSSI no larger than -52 dBm and 90\% links have RSSI smaller than -66 dBm.
These practical issues bring about great limitation of adaptive-threshold methods.

%If a high $T_{max}$ is wanted, the system needs to be deployed in a very dense manner for a high RSSI of links.
%However, the additional transmitting power consumption caused by this method could be much more than the wasted energy brought by false wakeup problem.
%Therefore, deploying a dense system and increasing transmitting power just for a good $T_{max}$ is not energy-efficient.
%If not modifying $T_{max}$, the improvement of adaptive methods is limited.
%This tradeoff could not be mediated because of the intrinsic feature of threshold-based methods.

%Based on above analysis, the limitation of threshold-based methods is presented.
Our key insight is that the energy efficient LPL should wake up nodes only when there is a ZigBee transmission rather than when there is high energy on the channel.
Above observations and analysis disclose the threshold-based methods cannot achieve this goal because of the limited threshold adaption scope.
If replacing energy detection by signal detection as the criterion of busy channel of CCA, the false wakeup problem will be much less no matter the strength of noise is above or below the optimal threshold.
%We will demonstrate the method IALPL adopts for achieving this goal in the following section, based on our empirical observations.



\subsection{Analysis of LPP}
\label{subsec:analysisRMAC}

Receiver-initiated MAC protocols propose Low Power Probing (LPP) to avoid the long preambles of sender-initiated LPL.
In receiver-initiated MAC protocols, the communication is triggered by receiver.
When a sender has a packet to transmit, it remains awake, waiting the intended receiver wakes up.
Receiver periodically turns on its radio and sends out a probe to announce it is awake.
Upon receiving a probe, sender sends out the ACK and the data packet.
If the receiver does not get any ACK for certain time, it goes back to sleep.
Since the trigger of staying awake in receiver-initiated MAC protocols is probe packets, nodes will not encounter with the false wakeup problem.

However, receiver-initiated MAC protocols tend to have high baseline energy consumption due to the transmission of probe packets, especially in low-data rate networks.
What's worse, when link quality is not good enough, probe packets are easy to get lost, resulting in long additional radio-on time on sender node.
We conduct an experiment of studying duty cycle of A-MAC \cite{bib:AMAC}, the state-of-the-art receiver-initiated MAC protocol.
Receiver sends a probe every 512 ms and sender generates a packet every 2 seconds.
Figure \ref{fig:dutyCycleAMAC} presents the duty cycle of the sender with A-MAC under virous link qualities conditions.
When the link quality is good, $RSSI \in [-60,-40]$, probes are acked even it is only transmitted once.
Hence, the duty cycle stays low and invariant.
With the link quality gets worse, the probe packets start getting lost and the duty cycle increases quickly since each lost probe packet makes sender has to wait for another sleep interval.



